U&P AI – Natural Language Processing with Python

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Be efficient in Python programming & boost your skills with the U&P AI – Natural Language Processing with Python course. This course has been specially designed to help learners gain a good command of U&P AI – Natural Language Processing with Python, providing them with a solid foundation of knowledge to become a qualified professional.

With this course, containing on-demand video lectures and downloadable resources and affordable premium-quality E-learning content, you can learn at your own pace. The U&P AI – Natural Language Processing with Python covers the NPL, WordNet, Word2Vec applications which will allow you to enhance your CV, impress potential employers, and stand out from the crowd. The course will equip you with everything you need to succeed.

If you are looking for up-to-date knowledge and techniques that will ensure you have the most in-demand skills to rise to the top of the tech industry, this is the right place. Enrol in U&P AI – Natural Language Processing with Python course today and learn from the very best the industry has to offer!

Gain ideas about NLP and its applications
Familiar yourself with Feature Engineering
Learn about Corpuses & Frequency Distribution
Understand the WordNet and Word2Vec
Learn how to create vocabulary for any NLP Model
Explore and represent the Topic of Natural Language Text

6+ hours of on-demand video lectures and downloadable resources.
Affordable premium-quality E-learning content, you can learn at your own pace.
You will receive a completion certificate upon completing the course.
Internationally recognized Accredited Qualification will boost up your resume.
You will learn the researched and proven approach adopted by successful salespeople to transform their careers.
You will be able to incorporate various practical sales techniques successfully and understand your customers better.

Curriculum

Unit 01: Getting an Idea of NLP and its Applications

Module 01: Introduction to NLP

00:03:00
Module 02: By the End of This Section

00:01:00
Module 03: Installation

00:04:00
Module 04: Tips

00:01:00
Module 05: U – Tokenization

00:01:00
Module 06: P – Tokenization

00:02:00
Module 07: U – Stemming

00:02:00
Module 08: P – Stemming

00:05:00
Module 09: U – Lemmatization

00:02:00
Module 10: P – Lemmatization

00:03:00
Module 11: U – Chunks

00:02:00
Module 12: P – Chunks

00:05:00
Module 13: U – Bag of Words

00:04:00
Module 14: P – Bag of Words

00:04:00
Module 15: U – Category Predictor

00:05:00
Module 16: P – Category Predictor

00:06:00
Module 17: U – Gender Identifier

00:01:00
Module 18: P – Gender Identifier

00:08:00
Module 19: U – Sentiment Analyzer

00:02:00
Module 20: P – Sentiment Analyzer

00:07:00
Module 21: U – Topic Modeling

00:03:00
Module 22: P – Topic Modeling

00:06:00
Module 23: Summary

00:01:00

Unit 02: Feature Engineering

Module 01: Introduction

00:02:00
Module 02: One Hot Encoding

00:02:00
Module 03: Count Vectorizer

00:04:00
Module 04: N-grams

00:04:00
Module 05: Hash Vectorizing

00:02:00
Module 06: Word Embedding

00:11:00
Module 07: FastText

00:04:00

Unit 03: Dealing with corpus and WordNet

Module 01: Introduction

00:01:00
Module 02: In-built corpora

00:06:00
Module 03: External Corpora

00:08:00
Module 04: Corpuses & Frequency Distribution

00:07:00
Module 05: Frequency Distribution

00:06:00
Module 06: WordNet

00:06:00
Module 07: Wordnet with Hyponyms and Hypernyms

00:07:00
Module 08: The Average according to WordNet

00:07:00

Unit 04: Create your Vocabulary for any NLP Model

Module 01: Introduction and Challenges

00:08:00
Module 02: Building your Vocabulary Part-01

00:02:00
Module 03: Building your Vocabulary Part-02

00:03:00
Module 04: Building your Vocabulary Part-03

00:07:00
Module 05: Building your Vocabulary Part-04

00:12:00
Module 06: Building your Vocabulary Part-05

00:06:00
Module 07: Dot Product

00:03:00
Module 08: Similarity using Dot Product

00:03:00
Module 09: Reducing Dimensions of your Vocabulary using token improvement

00:02:00
Module 10: Reducing Dimensions of your Vocabulary using n-grams

00:10:00
Module 11: Reducing Dimensions of your Vocabulary using normalizing

00:10:00
Module 12: Reducing Dimensions of your Vocabulary using case normalization

00:05:00
Module 13: When to use stemming and lemmatization?

00:04:00
Module 14: Sentiment Analysis Overview

00:05:00
Module 15: Two approaches for sentiment analysis

00:03:00
Module 16: Sentiment Analysis using rule-based

00:05:00
Module 17: Sentiment Analysis using machine learning – 1

00:10:00
Module 18: Sentiment Analysis using machine learning – 2

00:04:00
Module 19: Summary

00:01:00

Unit 05: Word2Vec in Detail and what is going on under the hood

Module 01: Introduction

00:04:00
Module 02: Bag of words in detail

00:14:00
Module 03: Vectorizing

00:08:00
Module 04: Vectorizing and Cosine Similarity

00:11:00
Module 05: Topic modeling in Detail

00:16:00
Module 06: Make your Vectors will more reflect the Meaning, or Topic, of the Document

00:10:00
Module 07: Sklearn in a short way

00:03:00
Module 08: Summary

00:02:00